Fuzzy logic systems are based upon a control methodology intended to emulate and exploit the vagueness inherent in the human thinking process. The "fuzzy" approach to analyzing complex systems and decision processes is described by L. A. Zadeh in the IEEE Transactions on Systems, January 1973, vol. SMC-3, no. 1, pp. 28-44 and IEEE Spectrum, August 1984, pp. 26-32.
Fuzzy logic bases decisions on input in the form of linguistic variables with malleable boundaries instead of conventional quantitative variables with firm boundaries. For instance, instead of describing a typical summer's day in the southwest region of the United States as having a temperature of 95.degree. F. and a relative humidity of 90%, the fuzzy approach might described the weather linguistically as "hot and humid." This, of course, is more likely to be the way a human would describe the weather: not only in fuzzy or vague terms, but also by classifying the particular sensed conditions of temperature and humidity into previously experienced buckets or sets. Thus, the term "humid" might apply to relative humidity above 50%. Similarly, the term "hot" might apply to temperatures above 80.degree. F. These classifications can also be tempered by such qualifications as "somewhat" or "very."
Thus, by receiving inputs in the form of linguistic variables, fuzzy set theory provides a method for an approximate characterization of complex or amorphous phenomena or processes. Data are assigned to fuzzy sets based upon the degree of membership therein, which ranges from 0 (no membership) to 1.0 (full membership). Fuzzy set theory uses membership functions to determine the fuzzy set or sets to which a particular data value belongs and its degree of membership therein. Such degree of membership is typically plotted in Z-, S-, and PI functions. This approach relaxes sensor accuracy requirements as opposed to traditional logic that places several constraints thereon. Fuzzy logic allows control decisions in spite of lack of absolute accuracy from sensor data. Nevertheless, the accuracy of the sensor would be sufficient to determine if the data belongs to a particular fuzzy set.
An inherent benefit of fuzzy systems is that sensor data is constantly being received and analyzed, notwithstanding system complexities and uncertainties. Accordingly, it is important that a practical fuzzy system have the capability to process this large volume of data and make appropriate adjustments and decisions preferably in real time. There have been some recent improvements in the prior art in effort to render fuzzy logic and set theory applicable to real time processes.
For example, in U.S. Pat. No. 4,809,175 issued to Hosaka, et al., is disclosed a vehicle control system and related method which performs analog to grade-of-membership conversions by way of software. In accordance with that invention, depending upon the particular hardware being used, several machine instruction cycles will occur for each membership grade determination. Since additional instructions are required for ascertaining by interpolation, values not contained in a memory-resident discourse table, response times are degraded, thereby rendering this art not applicable to high-speed real time fuzzy systems due to a tendency to overload. Another limitation of that invention is that the analog interface fails to provide an on-line rescaling capability for sensor data. Accordingly, in an application in which sensors are changed, the level converter's scaling hardware would necessarily require physical change-out. It should be apparent to those skilled in the art that such hardware change-outs are impractical for real time environments. In addition, the invention is not suitable for military or space applications because power surges would erase membership values which are stored in volatile memory, thereby necessitating reload and recalculation operations.
U.S. Pat. Nos. 4,694,418; 4,716,540; and 4,837,725, issued to Yamakawa, et al., disclose fuzzy membership function integrated circuitry which have high implementation costs because an actual working system is custom-built, thereby requiring additional hardware. The Yamakawa fuzzy logic integrated circuits lack the controls required for use in distributed high-speed and real time fuzzy systems. These devices generate analog output requiring an analog-to-digital conversion before being processed by a digital computer. Furthermore, these chips only provide for analog input signals. In addition, the Yamakawa integrated circuits do not provide for on-line raw data acquisition or the rescaling thereof relative to learning new processes when sensors are added or changed. Furthermore, these integrated circuits are not useful for space and military applications because they are not constructed for these environments.
Accordingly, these limitations and disadvantages of the prior art are overcome with the present invention, and improved means and techniques are provided which are especially useful for implementing embedded, high-throughput fuzzy systems in real time.